Fine-Tuning and Evaluating Vision AI Models

Building high-performing computer vision systems requires more than training a model—it requires careful evaluation, reliable predictions, and continuous refinement. In this course, you'll learn how to fine-tune and evaluate computer vision models used in real-world AI systems. You'll begin by applying transfer learning techniques to improve model accuracy on domain-specific datasets and analyzing learning-rate schedules to understand training behavior. Next, you'll evaluate the calibration of classification models and apply post-hoc correction methods to improve prediction reliability. The course also explores data preparation and annotation practices for object detection. You'll analyze object-size distributions to configure anchor boxes and evaluate detector performance using standard metrics. Finally, you'll examine image segmentation models. You'll learn how to address class imbalance, analyze segmentation errors, and apply post-processing techniques to improve prediction quality. By the end of the course, you'll be able to evaluate, diagnose, and refine computer vision models across classification, detection, and segmentation tasks

• Apply transfer learning and learning rate analysis to improve model accuracy
• Evaluate model calibration, annotation quality, and object detection metrics
• Diagnose segmentation errors and refine outputs with post-processing

• Basic familiarity with computer vision concepts
• Readiness for practical training and data handling